Overview

Dataset statistics

Number of variables65
Number of observations50289
Missing cells2016787
Missing cells (%)61.7%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory24.9 MiB
Average record size in memory520.0 B

Variable types

Categorical31
Unsupported27
Numeric7

Alerts

Fuel Type Code has constant value "ELEC" Constant
Country has constant value "US" Constant
Station Name has a high cardinality: 47830 distinct values High cardinality
Street Address has a high cardinality: 34671 distinct values High cardinality
Intersection Directions has a high cardinality: 2258 distinct values High cardinality
City has a high cardinality: 5357 distinct values High cardinality
State has a high cardinality: 53 distinct values High cardinality
ZIP has a high cardinality: 9477 distinct values High cardinality
Station Phone has a high cardinality: 8137 distinct values High cardinality
Expected Date has a high cardinality: 67 distinct values High cardinality
Access Days Time has a high cardinality: 1207 distinct values High cardinality
Updated At has a high cardinality: 278 distinct values High cardinality
Open Date has a high cardinality: 2833 distinct values High cardinality
Facility Type has a high cardinality: 60 distinct values High cardinality
EV Pricing has a high cardinality: 564 distinct values High cardinality
EV Level1 EVSE Num is highly correlated with EV Level2 EVSE Num and 1 other fieldsHigh correlation
EV Level2 EVSE Num is highly correlated with EV Level1 EVSE NumHigh correlation
EV DC Fast Count is highly correlated with EV Level1 EVSE NumHigh correlation
EV Level1 EVSE Num is highly correlated with EV Level2 EVSE Num and 1 other fieldsHigh correlation
EV Level2 EVSE Num is highly correlated with EV Level1 EVSE NumHigh correlation
EV DC Fast Count is highly correlated with EV Level1 EVSE NumHigh correlation
EV Level1 EVSE Num is highly correlated with EV Level2 EVSE Num and 1 other fieldsHigh correlation
EV Level2 EVSE Num is highly correlated with EV Level1 EVSE NumHigh correlation
EV DC Fast Count is highly correlated with EV Level1 EVSE NumHigh correlation
State is highly correlated with Expected Date and 12 other fieldsHigh correlation
Status Code is highly correlated with Expected Date and 4 other fieldsHigh correlation
Expected Date is highly correlated with State and 17 other fieldsHigh correlation
Groups With Access Code is highly correlated with Status Code and 14 other fieldsHigh correlation
Cards Accepted is highly correlated with State and 18 other fieldsHigh correlation
EV Level1 EVSE Num is highly correlated with Expected Date and 1 other fieldsHigh correlation
EV Level2 EVSE Num is highly correlated with EV Level1 EVSE NumHigh correlation
EV DC Fast Count is highly correlated with Expected Date and 5 other fieldsHigh correlation
EV Other Info is highly correlated with State and 7 other fieldsHigh correlation
EV Network is highly correlated with State and 14 other fieldsHigh correlation
EV Network Web is highly correlated with State and 15 other fieldsHigh correlation
Geocode Status is highly correlated with Cards Accepted and 8 other fieldsHigh correlation
Latitude is highly correlated with State and 4 other fieldsHigh correlation
Longitude is highly correlated with State and 6 other fieldsHigh correlation
Date Last Confirmed is highly correlated with State and 18 other fieldsHigh correlation
ID is highly correlated with Expected Date and 12 other fieldsHigh correlation
Owner Type Code is highly correlated with Expected Date and 10 other fieldsHigh correlation
Federal Agency ID is highly correlated with State and 8 other fieldsHigh correlation
Federal Agency Name is highly correlated with State and 10 other fieldsHigh correlation
EV Connector Types is highly correlated with Expected Date and 16 other fieldsHigh correlation
Groups With Access Code (French) is highly correlated with Status Code and 14 other fieldsHigh correlation
Access Code is highly correlated with Expected Date and 10 other fieldsHigh correlation
Access Detail Code is highly correlated with Expected Date and 12 other fieldsHigh correlation
Federal Agency Code is highly correlated with State and 10 other fieldsHigh correlation
Facility Type is highly correlated with State and 18 other fieldsHigh correlation
EV On-Site Renewable Source is highly correlated with State and 9 other fieldsHigh correlation
Intersection Directions has 47798 (95.0%) missing values Missing
Plus4 has 50289 (100.0%) missing values Missing
Station Phone has 3078 (6.1%) missing values Missing
Expected Date has 50142 (99.7%) missing values Missing
Access Days Time has 2056 (4.1%) missing values Missing
Cards Accepted has 48773 (97.0%) missing values Missing
BD Blends has 50289 (100.0%) missing values Missing
NG Fill Type Code has 50289 (100.0%) missing values Missing
NG PSI has 50289 (100.0%) missing values Missing
EV Level1 EVSE Num has 49288 (98.0%) missing values Missing
EV Level2 EVSE Num has 5432 (10.8%) missing values Missing
EV DC Fast Count has 44350 (88.2%) missing values Missing
EV Other Info has 50255 (99.9%) missing values Missing
EV Network Web has 8485 (16.9%) missing values Missing
Owner Type Code has 33756 (67.1%) missing values Missing
Federal Agency ID has 49235 (97.9%) missing values Missing
Federal Agency Name has 49235 (97.9%) missing values Missing
Hydrogen Status Link has 50289 (100.0%) missing values Missing
NG Vehicle Class has 50289 (100.0%) missing values Missing
LPG Primary has 50289 (100.0%) missing values Missing
E85 Blender Pump has 50289 (100.0%) missing values Missing
Intersection Directions (French) has 50289 (100.0%) missing values Missing
Access Days Time (French) has 50289 (100.0%) missing values Missing
BD Blends (French) has 50289 (100.0%) missing values Missing
Hydrogen Is Retail has 50289 (100.0%) missing values Missing
Access Detail Code has 46895 (93.3%) missing values Missing
Federal Agency Code has 49235 (97.9%) missing values Missing
Facility Type has 35474 (70.5%) missing values Missing
CNG Dispenser Num has 50289 (100.0%) missing values Missing
CNG On-Site Renewable Source has 50289 (100.0%) missing values Missing
CNG Total Compression Capacity has 50289 (100.0%) missing values Missing
CNG Storage Capacity has 50289 (100.0%) missing values Missing
LNG On-Site Renewable Source has 50289 (100.0%) missing values Missing
E85 Other Ethanol Blends has 50289 (100.0%) missing values Missing
EV Pricing has 35416 (70.4%) missing values Missing
EV Pricing (French) has 50289 (100.0%) missing values Missing
LPG Nozzle Types has 50289 (100.0%) missing values Missing
Hydrogen Pressures has 50289 (100.0%) missing values Missing
Hydrogen Standards has 50289 (100.0%) missing values Missing
CNG Fill Type Code has 50289 (100.0%) missing values Missing
CNG PSI has 50289 (100.0%) missing values Missing
CNG Vehicle Class has 50289 (100.0%) missing values Missing
LNG Vehicle Class has 50289 (100.0%) missing values Missing
EV On-Site Renewable Source has 49937 (99.3%) missing values Missing
Restricted Access has 50289 (100.0%) missing values Missing
EV Level2 EVSE Num is highly skewed (γ1 = 31.92539846) Skewed
Station Name is uniformly distributed Uniform
Intersection Directions is uniformly distributed Uniform
ID has unique values Unique
Plus4 is an unsupported type, check if it needs cleaning or further analysis Unsupported
BD Blends is an unsupported type, check if it needs cleaning or further analysis Unsupported
NG Fill Type Code is an unsupported type, check if it needs cleaning or further analysis Unsupported
NG PSI is an unsupported type, check if it needs cleaning or further analysis Unsupported
Hydrogen Status Link is an unsupported type, check if it needs cleaning or further analysis Unsupported
NG Vehicle Class is an unsupported type, check if it needs cleaning or further analysis Unsupported
LPG Primary is an unsupported type, check if it needs cleaning or further analysis Unsupported
E85 Blender Pump is an unsupported type, check if it needs cleaning or further analysis Unsupported
Intersection Directions (French) is an unsupported type, check if it needs cleaning or further analysis Unsupported
Access Days Time (French) is an unsupported type, check if it needs cleaning or further analysis Unsupported
BD Blends (French) is an unsupported type, check if it needs cleaning or further analysis Unsupported
Hydrogen Is Retail is an unsupported type, check if it needs cleaning or further analysis Unsupported
CNG Dispenser Num is an unsupported type, check if it needs cleaning or further analysis Unsupported
CNG On-Site Renewable Source is an unsupported type, check if it needs cleaning or further analysis Unsupported
CNG Total Compression Capacity is an unsupported type, check if it needs cleaning or further analysis Unsupported
CNG Storage Capacity is an unsupported type, check if it needs cleaning or further analysis Unsupported
LNG On-Site Renewable Source is an unsupported type, check if it needs cleaning or further analysis Unsupported
E85 Other Ethanol Blends is an unsupported type, check if it needs cleaning or further analysis Unsupported
EV Pricing (French) is an unsupported type, check if it needs cleaning or further analysis Unsupported
LPG Nozzle Types is an unsupported type, check if it needs cleaning or further analysis Unsupported
Hydrogen Pressures is an unsupported type, check if it needs cleaning or further analysis Unsupported
Hydrogen Standards is an unsupported type, check if it needs cleaning or further analysis Unsupported
CNG Fill Type Code is an unsupported type, check if it needs cleaning or further analysis Unsupported
CNG PSI is an unsupported type, check if it needs cleaning or further analysis Unsupported
CNG Vehicle Class is an unsupported type, check if it needs cleaning or further analysis Unsupported
LNG Vehicle Class is an unsupported type, check if it needs cleaning or further analysis Unsupported
Restricted Access is an unsupported type, check if it needs cleaning or further analysis Unsupported

Reproduction

Analysis started2022-02-18 19:31:05.306779
Analysis finished2022-02-18 19:31:24.111354
Duration18.8 seconds
Software versionpandas-profiling v3.1.0
Download configurationconfig.json

Variables

Fuel Type Code
Categorical

CONSTANT
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size393.0 KiB
ELEC
50289 

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowELEC
2nd rowELEC
3rd rowELEC
4th rowELEC
5th rowELEC

Common Values

ValueCountFrequency (%)
ELEC50289
100.0%

Length

2022-02-18T13:31:24.165209image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-02-18T13:31:24.219067image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
ValueCountFrequency (%)
elec50289
100.0%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Station Name
Categorical

HIGH CARDINALITY
UNIFORM

Distinct47830
Distinct (%)95.1%
Missing0
Missing (%)0.0%
Memory size393.0 KiB
Walmart
 
65
Whole Foods Market
 
64
Wawa - Tesla Supercharger
 
63
Target - Tesla Supercharger
 
53
Sheetz - Tesla Supercharger
 
51
Other values (47825)
49993 

Length

Max length116
Median length23
Mean length25.38185289
Min length2

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique46460 ?
Unique (%)92.4%

Sample

1st rowLADWP - Truesdale Center
2nd rowLADWP - West LA District Office
3rd rowLos Angeles Convention Center
4th rowLADWP - John Ferraro Building
5th rowLADWP - Haynes Power Plant

Common Values

ValueCountFrequency (%)
Walmart65
 
0.1%
Whole Foods Market64
 
0.1%
Wawa - Tesla Supercharger63
 
0.1%
Target - Tesla Supercharger53
 
0.1%
Sheetz - Tesla Supercharger51
 
0.1%
Meijer - Tesla Supercharger45
 
0.1%
Duke Energy38
 
0.1%
ComEd38
 
0.1%
Walgreens37
 
0.1%
Dunkin' Donuts23
 
< 0.1%
Other values (47820)49812
99.1%

Length

2022-02-18T13:31:24.675393image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
12927
 
5.9%
tesla5736
 
2.6%
15108
 
2.3%
station4887
 
2.2%
destination4462
 
2.0%
23525
 
1.6%
of2464
 
1.1%
city2235
 
1.0%
center2061
 
0.9%
garage1679
 
0.8%
Other values (24946)173916
79.4%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Street Address
Categorical

HIGH CARDINALITY

Distinct34671
Distinct (%)69.0%
Missing33
Missing (%)0.1%
Memory size393.0 KiB
1201 Pine St
 
81
2910 Tannery Way
 
79
Unnamed Road
 
61
806 S Airport Blvd
 
57
1 Facebook Way
 
57
Other values (34666)
49921 

Length

Max length63
Median length17
Mean length17.66292582
Min length3

Characters and Unicode

Total characters42
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique27240 ?
Unique (%)54.2%

Sample

1st row11797 Truesdale St
2nd row1394 S Sepulveda Blvd
3rd row1201 S Figueroa St
4th row111 N Hope St
5th row6801 E 2nd St

Common Values

ValueCountFrequency (%)
1201 Pine St81
 
0.2%
2910 Tannery Way79
 
0.2%
Unnamed Road61
 
0.1%
806 S Airport Blvd57
 
0.1%
1 Facebook Way57
 
0.1%
150 W Tasman Dr40
 
0.1%
1 Cyclotron Rd37
 
0.1%
309 Constitution Dr36
 
0.1%
San Francisco International Airport Domestic35
 
0.1%
2595 Augustine Dr34
 
0.1%
Other values (34661)49739
98.9%
(Missing)33
 
0.1%

Length

2022-02-18T13:31:24.942072image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
st11240
 
6.1%
ave7196
 
3.9%
rd6909
 
3.8%
dr5134
 
2.8%
blvd4410
 
2.4%
n3410
 
1.9%
w3376
 
1.8%
s3349
 
1.8%
e3121
 
1.7%
way1558
 
0.8%
Other values (18776)134199
73.0%

Most occurring characters

ValueCountFrequency (%)
42
100.0%

Most occurring categories

ValueCountFrequency (%)
Control42
100.0%

Most frequent character per category

Control
ValueCountFrequency (%)
42
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common42
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
42
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII42
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
42
100.0%

Intersection Directions
Categorical

HIGH CARDINALITY
MISSING
UNIFORM

Distinct2258
Distinct (%)90.6%
Missing47798
Missing (%)95.0%
Memory size393.0 KiB
Located in the parking lot
 
12
Roslyn St and E 53rd Place
 
10
Chargers are under repair.
 
8
Parking Lot
 
7
Located behind the building
 
7
Other values (2253)
2447 

Length

Max length507
Median length40
Mean length51.31272581
Min length1

Characters and Unicode

Total characters1257
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2109 ?
Unique (%)84.7%

Sample

1st rowWest hall and South hall
2nd rowAcross Hope
3rd rowAt 12th and N St
4th rowAt B St
5th rowPatient Parking Structure, level G

Common Values

ValueCountFrequency (%)
Located in the parking lot12
 
< 0.1%
Roslyn St and E 53rd Place10
 
< 0.1%
Chargers are under repair. 8
 
< 0.1%
Parking Lot 7
 
< 0.1%
Located behind the building7
 
< 0.1%
Charger under repair. 6
 
< 0.1%
Attendant will assist in charging vehicle. Parking rates apply. Inquire within. 6
 
< 0.1%
. 6
 
< 0.1%
Located in parking lot6
 
< 0.1%
Chargers under repair. 6
 
< 0.1%
Other values (2248)2417
 
4.8%
(Missing)47798
95.0%

Length

2022-02-18T13:31:25.079042image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
the1602
 
7.1%
located982
 
4.4%
of938
 
4.2%
parking791
 
3.5%
on584
 
2.6%
in580
 
2.6%
and524
 
2.3%
lot439
 
1.9%
garage326
 
1.4%
are312
 
1.4%
Other values (2776)15487
68.6%

Most occurring characters

ValueCountFrequency (%)
1257
100.0%

Most occurring categories

ValueCountFrequency (%)
Control1257
100.0%

Most frequent character per category

Control
ValueCountFrequency (%)
1257
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common1257
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1257
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII1257
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1257
100.0%

City
Categorical

HIGH CARDINALITY

Distinct5357
Distinct (%)10.7%
Missing0
Missing (%)0.0%
Memory size393.0 KiB
Los Angeles
 
1400
San Diego
 
674
Atlanta
 
634
San Jose
 
572
Irvine
 
571
Other values (5352)
46438 

Length

Max length33
Median length9
Mean length9.017140925
Min length2

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1910 ?
Unique (%)3.8%

Sample

1st rowSun Valley
2nd rowLos Angeles
3rd rowLos Angeles
4th rowLos Angeles
5th rowLong Beach

Common Values

ValueCountFrequency (%)
Los Angeles1400
 
2.8%
San Diego674
 
1.3%
Atlanta634
 
1.3%
San Jose572
 
1.1%
Irvine571
 
1.1%
Austin520
 
1.0%
San Francisco499
 
1.0%
Kansas City452
 
0.9%
Seattle404
 
0.8%
Boston378
 
0.8%
Other values (5347)44185
87.9%

Length

2022-02-18T13:31:25.206291image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
san2578
 
3.8%
city1795
 
2.6%
los1477
 
2.2%
angeles1413
 
2.1%
park998
 
1.5%
beach961
 
1.4%
santa925
 
1.4%
diego674
 
1.0%
francisco646
 
0.9%
atlanta634
 
0.9%
Other values (4502)56014
82.2%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

State
Categorical

HIGH CARDINALITY
HIGH CORRELATION

Distinct53
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size393.0 KiB
CA
14651 
NY
2977 
FL
2665 
TX
 
2319
MA
 
2198
Other values (48)
25479 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowCA
2nd rowCA
3rd rowCA
4th rowCA
5th rowCA

Common Values

ValueCountFrequency (%)
CA14651
29.1%
NY2977
 
5.9%
FL2665
 
5.3%
TX2319
 
4.6%
MA2198
 
4.4%
WA1775
 
3.5%
CO1627
 
3.2%
GA1588
 
3.2%
MD1263
 
2.5%
PA1166
 
2.3%
Other values (43)18060
35.9%

Length

2022-02-18T13:31:25.311049image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
ca14651
29.1%
ny2977
 
5.9%
fl2665
 
5.3%
tx2319
 
4.6%
ma2198
 
4.4%
wa1775
 
3.5%
co1627
 
3.2%
ga1588
 
3.2%
md1263
 
2.5%
pa1166
 
2.3%
Other values (43)18060
35.9%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

ZIP
Categorical

HIGH CARDINALITY

Distinct9477
Distinct (%)18.8%
Missing0
Missing (%)0.0%
Memory size393.0 KiB
94025
 
372
95054
 
258
92618
 
223
94080
 
148
92802
 
137
Other values (9472)
49151 

Length

Max length6
Median length5
Mean length5.000278391
Min length5

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3215 ?
Unique (%)6.4%

Sample

1st row91352
2nd row90024
3rd row90015
4th row90012
5th row90803

Common Values

ValueCountFrequency (%)
94025372
 
0.7%
95054258
 
0.5%
92618223
 
0.4%
94080148
 
0.3%
92802137
 
0.3%
94607128
 
0.3%
94128124
 
0.2%
92101120
 
0.2%
90802118
 
0.2%
95814114
 
0.2%
Other values (9467)48547
96.5%

Length

2022-02-18T13:31:25.399935image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
94025372
 
0.7%
95054258
 
0.5%
92618223
 
0.4%
94080148
 
0.3%
92802137
 
0.3%
94607128
 
0.3%
94128124
 
0.2%
92101120
 
0.2%
90802118
 
0.2%
95814114
 
0.2%
Other values (9454)48548
96.5%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Plus4
Unsupported

MISSING
REJECTED
UNSUPPORTED

Missing50289
Missing (%)100.0%
Memory size393.0 KiB

Station Phone
Categorical

HIGH CARDINALITY
MISSING

Distinct8137
Distinct (%)17.2%
Missing3078
Missing (%)6.1%
Memory size393.0 KiB
888-758-4389
27189 
800-663-5633
 
1916
888-998-2546
 
1579
877-798-3752
 
1258
855-900-7584
 
1019
Other values (8132)
14250 

Length

Max length40
Median length12
Mean length13.37569634
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique7016 ?
Unique (%)14.9%

Sample

1st row213-741-1151
2nd row626-575-6800
3rd row800-256-6219
4th row951-351-3110
5th row626-744-7665

Common Values

ValueCountFrequency (%)
888-758-438927189
54.1%
800-663-56331916
 
3.8%
888-998-25461579
 
3.1%
877-798-37521258
 
2.5%
855-900-75841019
 
2.0%
877-455-3833868
 
1.7%
888-264-2208811
 
1.6%
833-632-2778737
 
1.5%
877-798-3752 877-798-3752659
 
1.3%
866-816-7584645
 
1.3%
Other values (8127)10530
 
20.9%
(Missing)3078
 
6.1%

Length

2022-02-18T13:31:25.504657image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
888-758-438927192
52.4%
877-798-37526357
 
12.3%
800-663-56331916
 
3.7%
888-998-25461579
 
3.0%
855-900-75841035
 
2.0%
877-455-3833868
 
1.7%
888-264-2208861
 
1.7%
833-632-2778737
 
1.4%
866-816-7584679
 
1.3%
888-356-8911262
 
0.5%
Other values (8063)10407
 
20.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Status Code
Categorical

HIGH CORRELATION

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size393.0 KiB
E
50142 
T
 
79
P
 
68

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowE
2nd rowE
3rd rowE
4th rowE
5th rowE

Common Values

ValueCountFrequency (%)
E50142
99.7%
T79
 
0.2%
P68
 
0.1%

Length

2022-02-18T13:31:25.607455image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-02-18T13:31:25.671978image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
ValueCountFrequency (%)
e50142
99.7%
t79
 
0.2%
p68
 
0.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Expected Date
Categorical

HIGH CARDINALITY
HIGH CORRELATION
MISSING

Distinct67
Distinct (%)45.6%
Missing50142
Missing (%)99.7%
Memory size393.0 KiB
2022-11-01
12 
2022-03-01
 
9
2022-03-15
 
8
2022-11-24
 
7
2021-07-31
 
6
Other values (62)
105 

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique44 ?
Unique (%)29.9%

Sample

1st row2021-09-01
2nd row2022-01-13
3rd row2021-12-15
4th row2022-03-31
5th row2022-01-01

Common Values

ValueCountFrequency (%)
2022-11-0112
 
< 0.1%
2022-03-019
 
< 0.1%
2022-03-158
 
< 0.1%
2022-11-247
 
< 0.1%
2021-07-316
 
< 0.1%
2021-06-306
 
< 0.1%
2022-01-015
 
< 0.1%
2021-01-315
 
< 0.1%
2021-11-015
 
< 0.1%
2021-08-315
 
< 0.1%
Other values (57)79
 
0.2%
(Missing)50142
99.7%

Length

2022-02-18T13:31:25.738836image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2022-11-0112
 
8.2%
2022-03-019
 
6.1%
2022-03-158
 
5.4%
2022-11-247
 
4.8%
2021-07-316
 
4.1%
2021-06-306
 
4.1%
2021-11-015
 
3.4%
2022-03-315
 
3.4%
2021-08-315
 
3.4%
2021-01-315
 
3.4%
Other values (57)79
53.7%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Groups With Access Code
Categorical

HIGH CORRELATION

Distinct22
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size393.0 KiB
Public
43929 
Private
 
2844
Public - Call ahead
 
1608
Public - Credit card at all times
 
1003
Private - Government only
 
709
Other values (17)
 
196

Length

Max length64
Median length6
Mean length7.39720416
Min length6

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique6 ?
Unique (%)< 0.1%

Sample

1st rowPrivate
2nd rowPrivate
3rd rowPublic
4th rowPrivate
5th rowPrivate

Common Values

ValueCountFrequency (%)
Public43929
87.4%
Private2844
 
5.7%
Public - Call ahead1608
 
3.2%
Public - Credit card at all times1003
 
2.0%
Private - Government only709
 
1.4%
TEMPORARILY UNAVAILABLE (Public)56
 
0.1%
PLANNED - not yet accessible (Public)52
 
0.1%
Private - Credit card at all times24
 
< 0.1%
PLANNED - not yet accessible (Public - Credit card at all times)13
 
< 0.1%
Public - Card key at all times12
 
< 0.1%
Other values (12)39
 
0.1%

Length

2022-02-18T13:31:25.835192image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
public46686
72.8%
private3603
 
5.6%
3462
 
5.4%
call1619
 
2.5%
ahead1619
 
2.5%
card1057
 
1.6%
at1054
 
1.6%
all1054
 
1.6%
times1054
 
1.6%
credit1044
 
1.6%
Other values (14)1891
 
2.9%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Access Days Time
Categorical

HIGH CARDINALITY
MISSING

Distinct1207
Distinct (%)2.5%
Missing2056
Missing (%)4.1%
Memory size393.0 KiB
24 hours daily
33188 
24 hours daily; for customer use only; see front desk for access
 
2173
MON: 24 hours | TUE: 24 hours | WED: 24 hours | THU: 24 hours | FRI: 24 hours | SAT: 24 hours | SUN: 24 hours
 
1550
MO: Not Specified; TU: Not Specified; WE: Not Specified; TH: Not Specified; FR: Not Specified; SA: Not Specified; SU: Not Specified
 
1475
Dealership business hours
 
1171
Other values (1202)
8676 

Length

Max length270
Median length14
Mean length29.9242842
Min length8

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique842 ?
Unique (%)1.7%

Sample

1st rowFleet use only
2nd row24 hours daily; pay lot
3rd rowFor fleet and employee use only
4th rowFleet use only
5th rowPermitted state legislators only

Common Values

ValueCountFrequency (%)
24 hours daily33188
66.0%
24 hours daily; for customer use only; see front desk for access2173
 
4.3%
MON: 24 hours | TUE: 24 hours | WED: 24 hours | THU: 24 hours | FRI: 24 hours | SAT: 24 hours | SUN: 24 hours1550
 
3.1%
MO: Not Specified; TU: Not Specified; WE: Not Specified; TH: Not Specified; FR: Not Specified; SA: Not Specified; SU: Not Specified1475
 
2.9%
Dealership business hours1171
 
2.3%
24 hours daily; for Tesla use only1157
 
2.3%
24 hours daily; for customer use only961
 
1.9%
6am-12am daily602
 
1.2%
24 hours daily; for customer use only; see valet for access420
 
0.8%
MO: 12:00am-12:00am; TU: 12:00am-12:00am; WE: 12:00am-12:00am; TH: 12:00am-12:00am; FR: 12:00am-12:00am; SA: 12:00am-12:00am; SU: 12:00am-12:00am340
 
0.7%
Other values (1197)5196
 
10.3%
(Missing)2056
 
4.1%

Length

2022-02-18T13:31:25.958546image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
hours52216
18.7%
2450461
18.1%
daily40707
14.6%
15950
 
5.7%
not10346
 
3.7%
specified10343
 
3.7%
for8644
 
3.1%
only6389
 
2.3%
use6333
 
2.3%
customer3718
 
1.3%
Other values (900)73433
26.4%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Cards Accepted
Categorical

HIGH CORRELATION
MISSING

Distinct45
Distinct (%)3.0%
Missing48773
Missing (%)97.0%
Memory size393.0 KiB
A D Debit M V
739 
A D M V
467 
Proprietor
 
64
A APPLE_PAY D M V
 
41
Checks
 
33
Other values (40)
172 

Length

Max length63
Median length13
Mean length11.03627968
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique18 ?
Unique (%)1.2%

Sample

1st rowComdata
2nd rowM V
3rd rowA D M V
4th rowA Cash D M V
5th rowCash

Common Values

ValueCountFrequency (%)
A D Debit M V739
 
1.5%
A D M V467
 
0.9%
Proprietor64
 
0.1%
A APPLE_PAY D M V41
 
0.1%
Checks33
 
0.1%
A Cash Checks D M V21
 
< 0.1%
D Debit M V16
 
< 0.1%
M V13
 
< 0.1%
CFN10
 
< 0.1%
Proprietor Wright_Exp10
 
< 0.1%
Other values (35)102
 
0.2%
(Missing)48773
97.0%

Length

2022-02-18T13:31:26.089196image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
m1368
21.0%
v1363
20.9%
d1332
20.5%
a1326
20.4%
debit800
12.3%
proprietor75
 
1.2%
cash69
 
1.1%
checks67
 
1.0%
apple_pay43
 
0.7%
credit21
 
0.3%
Other values (9)48
 
0.7%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

BD Blends
Unsupported

MISSING
REJECTED
UNSUPPORTED

Missing50289
Missing (%)100.0%
Memory size393.0 KiB

NG Fill Type Code
Unsupported

MISSING
REJECTED
UNSUPPORTED

Missing50289
Missing (%)100.0%
Memory size393.0 KiB

NG PSI
Unsupported

MISSING
REJECTED
UNSUPPORTED

Missing50289
Missing (%)100.0%
Memory size393.0 KiB

EV Level1 EVSE Num
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct38
Distinct (%)3.8%
Missing49288
Missing (%)98.0%
Infinite0
Infinite (%)0.0%
Mean3.372627373
Minimum1
Maximum90
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size393.0 KiB
2022-02-18T13:31:26.202894image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q32
95-th percentile12
Maximum90
Range89
Interquartile range (IQR)1

Descriptive statistics

Standard deviation7.279148988
Coefficient of variation (CV)2.158302173
Kurtosis45.03166558
Mean3.372627373
Median Absolute Deviation (MAD)0
Skewness5.976370363
Sum3376
Variance52.98600999
MonotonicityNot monotonic
2022-02-18T13:31:26.310604image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram with fixed size bins (bins=38)
ValueCountFrequency (%)
1585
 
1.2%
2196
 
0.4%
461
 
0.1%
333
 
0.1%
1018
 
< 0.1%
615
 
< 0.1%
514
 
< 0.1%
1212
 
< 0.1%
810
 
< 0.1%
76
 
< 0.1%
Other values (28)51
 
0.1%
(Missing)49288
98.0%
ValueCountFrequency (%)
1585
1.2%
2196
 
0.4%
333
 
0.1%
461
 
0.1%
514
 
< 0.1%
615
 
< 0.1%
76
 
< 0.1%
810
 
< 0.1%
92
 
< 0.1%
1018
 
< 0.1%
ValueCountFrequency (%)
901
< 0.1%
711
< 0.1%
601
< 0.1%
542
< 0.1%
511
< 0.1%
492
< 0.1%
471
< 0.1%
422
< 0.1%
401
< 0.1%
391
< 0.1%

EV Level2 EVSE Num
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING
SKEWED

Distinct62
Distinct (%)0.1%
Missing5432
Missing (%)10.8%
Infinite0
Infinite (%)0.0%
Mean2.311835388
Minimum1
Maximum311
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size393.0 KiB
2022-02-18T13:31:26.428277image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median2
Q32
95-th percentile4
Maximum311
Range310
Interquartile range (IQR)0

Descriptive statistics

Standard deviation3.262845021
Coefficient of variation (CV)1.411365635
Kurtosis2153.600517
Mean2.311835388
Median Absolute Deviation (MAD)0
Skewness31.92539846
Sum103702
Variance10.64615763
MonotonicityNot monotonic
2022-02-18T13:31:26.541045image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
228644
57.0%
110051
 
20.0%
42226
 
4.4%
31813
 
3.6%
6650
 
1.3%
5302
 
0.6%
8298
 
0.6%
10239
 
0.5%
1298
 
0.2%
787
 
0.2%
Other values (52)449
 
0.9%
(Missing)5432
 
10.8%
ValueCountFrequency (%)
110051
 
20.0%
228644
57.0%
31813
 
3.6%
42226
 
4.4%
5302
 
0.6%
6650
 
1.3%
787
 
0.2%
8298
 
0.6%
978
 
0.2%
10239
 
0.5%
ValueCountFrequency (%)
3111
< 0.1%
1561
< 0.1%
1281
< 0.1%
1231
< 0.1%
1081
< 0.1%
1061
< 0.1%
1002
< 0.1%
911
< 0.1%
811
< 0.1%
731
< 0.1%

EV DC Fast Count
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct31
Distinct (%)0.5%
Missing44350
Missing (%)88.2%
Infinite0
Infinite (%)0.0%
Mean3.736150867
Minimum1
Maximum56
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size393.0 KiB
2022-02-18T13:31:26.652447image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median2
Q34
95-th percentile12
Maximum56
Range55
Interquartile range (IQR)3

Descriptive statistics

Standard deviation4.320549962
Coefficient of variation (CV)1.156417424
Kurtosis14.67916229
Mean3.736150867
Median Absolute Deviation (MAD)1
Skewness2.814443221
Sum22189
Variance18.66715197
MonotonicityNot monotonic
2022-02-18T13:31:26.745236image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
12846
 
5.7%
2767
 
1.5%
8714
 
1.4%
4678
 
1.3%
6214
 
0.4%
12186
 
0.4%
3170
 
0.3%
10136
 
0.3%
1670
 
0.1%
2044
 
0.1%
Other values (21)114
 
0.2%
(Missing)44350
88.2%
ValueCountFrequency (%)
12846
5.7%
2767
 
1.5%
3170
 
0.3%
4678
 
1.3%
515
 
< 0.1%
6214
 
0.4%
78
 
< 0.1%
8714
 
1.4%
93
 
< 0.1%
10136
 
0.3%
ValueCountFrequency (%)
562
< 0.1%
403
< 0.1%
361
 
< 0.1%
351
 
< 0.1%
341
 
< 0.1%
321
 
< 0.1%
301
 
< 0.1%
283
< 0.1%
262
< 0.1%
251
 
< 0.1%

EV Other Info
Categorical

HIGH CORRELATION
MISSING

Distinct13
Distinct (%)38.2%
Missing50255
Missing (%)99.9%
Memory size393.0 KiB
1 SP Inductive
19 
1 LP Inductive
1 LP Inductive 1 SP Inductive 1 Avcon Conductive 1 Other Conductive
 
1
2 Conductive 240V
 
1
3 SP Inductive
 
1
Other values (8)

Length

Max length70
Median length14
Mean length18.55882353
Min length14

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique11 ?
Unique (%)32.4%

Sample

1st row1 LP Inductive 1 SP Inductive 1 Avcon Conductive 1 Other Conductive
2nd row1 LP Inductive
3rd row1 SP Inductive
4th row1 SP Inductive
5th row1 SP Inductive

Common Values

ValueCountFrequency (%)
1 SP Inductive19
 
< 0.1%
1 LP Inductive4
 
< 0.1%
1 LP Inductive 1 SP Inductive 1 Avcon Conductive 1 Other Conductive1
 
< 0.1%
2 Conductive 240V1
 
< 0.1%
3 SP Inductive1
 
< 0.1%
1 SP Inductive 1 Tesla Conductive1
 
< 0.1%
2 SP Inductive 1 Avcon Conductive 1 Tesla Conductive1
 
< 0.1%
2 SP Inductive1
 
< 0.1%
2 Tesla Conductive1
 
< 0.1%
1 Tesla Conductive1
 
< 0.1%
Other values (3)3
 
< 0.1%
(Missing)50255
99.9%

Length

2022-02-18T13:31:26.852699image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
134
27.6%
inductive29
23.6%
sp24
19.5%
conductive12
 
9.8%
lp5
 
4.1%
25
 
4.1%
tesla4
 
3.3%
120v3
 
2.4%
avcon2
 
1.6%
240v2
 
1.6%
Other values (3)3
 
2.4%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

EV Network
Categorical

HIGH CORRELATION

Distinct24
Distinct (%)< 0.1%
Missing5
Missing (%)< 0.1%
Memory size393.0 KiB
ChargePoint Network
27285 
Non-Networked
8480 
Tesla Destination
4437 
SemaCharge Network
 
1932
Blink Network
 
1576
Other values (19)
6574 

Length

Max length19
Median length19
Mean length16.23715297
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowNon-Networked
2nd rowNon-Networked
3rd rowNon-Networked
4th rowNon-Networked
5th rowNon-Networked

Common Values

ValueCountFrequency (%)
ChargePoint Network27285
54.3%
Non-Networked8480
 
16.9%
Tesla Destination4437
 
8.8%
SemaCharge Network1932
 
3.8%
Blink Network1576
 
3.1%
Tesla1249
 
2.5%
Greenlots1036
 
2.1%
Volta929
 
1.8%
EV Connect896
 
1.8%
eVgo Network883
 
1.8%
Other values (14)1581
 
3.1%

Length

2022-02-18T13:31:26.953503image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
network31676
36.0%
chargepoint27285
31.0%
non-networked8480
 
9.6%
tesla5686
 
6.5%
destination4437
 
5.0%
semacharge1932
 
2.2%
blink1576
 
1.8%
greenlots1036
 
1.2%
volta929
 
1.1%
ev896
 
1.0%
Other values (17)4099
 
4.7%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

EV Network Web
Categorical

HIGH CORRELATION
MISSING

Distinct23
Distinct (%)0.1%
Missing8485
Missing (%)16.9%
Memory size393.0 KiB
http://www.chargepoint.com/
27285 
https://www.tesla.com/destination-charging
4437 
https://semaconnect.com/
 
1932
http://www.blinkcharging.com/
 
1576
https://www.tesla.com/supercharger
 
1249
Other values (18)
5325 

Length

Max length48
Median length27
Mean length28.3236054
Min length15

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowhttps://voltacharging.com/
2nd rowhttps://voltacharging.com/
3rd rowhttp://evconnect.com/
4th rowhttp://evconnect.com/
5th rowhttp://evconnect.com/

Common Values

ValueCountFrequency (%)
http://www.chargepoint.com/27285
54.3%
https://www.tesla.com/destination-charging4437
 
8.8%
https://semaconnect.com/1932
 
3.8%
http://www.blinkcharging.com/1576
 
3.1%
https://www.tesla.com/supercharger1249
 
2.5%
http://greenlots.com/1036
 
2.1%
https://voltacharging.com/929
 
1.8%
http://evconnect.com/896
 
1.8%
https://www.evgo.com/883
 
1.8%
https://www.electrifyamerica.com/738
 
1.5%
Other values (13)843
 
1.7%
(Missing)8485
 
16.9%

Length

2022-02-18T13:31:27.064203image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
http://www.chargepoint.com27285
65.3%
https://www.tesla.com/destination-charging4437
 
10.6%
https://semaconnect.com1932
 
4.6%
http://www.blinkcharging.com1576
 
3.8%
https://www.tesla.com/supercharger1249
 
3.0%
http://greenlots.com1036
 
2.5%
https://voltacharging.com929
 
2.2%
http://evconnect.com896
 
2.1%
https://www.evgo.com883
 
2.1%
https://www.electrifyamerica.com738
 
1.8%
Other values (13)843
 
2.0%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Geocode Status
Categorical

HIGH CORRELATION

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size393.0 KiB
GPS
40692 
200-9
7101 
200-8
 
2472
200-6
 
23
200-5
 
1

Length

Max length5
Median length3
Mean length3.381673925
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowGPS
2nd row200-8
3rd rowGPS
4th rowGPS
5th rowGPS

Common Values

ValueCountFrequency (%)
GPS40692
80.9%
200-97101
 
14.1%
200-82472
 
4.9%
200-623
 
< 0.1%
200-51
 
< 0.1%

Length

2022-02-18T13:31:27.170161image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-02-18T13:31:27.247110image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
ValueCountFrequency (%)
gps40692
80.9%
200-97101
 
14.1%
200-82472
 
4.9%
200-623
 
< 0.1%
200-51
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Latitude
Real number (ℝ≥0)

HIGH CORRELATION

Distinct47867
Distinct (%)95.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean37.66494856
Minimum18.334138
Maximum64.852466
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size393.0 KiB
2022-02-18T13:31:27.457635image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Quantile statistics

Minimum18.334138
5-th percentile28.5841796
Q134.024105
median37.934644
Q341.12267
95-th percentile45.46920744
Maximum64.852466
Range46.518328
Interquartile range (IQR)7.098565

Descriptive statistics

Standard deviation5.01232674
Coefficient of variation (CV)0.1330766915
Kurtosis0.616445806
Mean37.66494856
Median Absolute Deviation (MAD)3.8211531
Skewness-0.299306678
Sum1894132.598
Variance25.12341935
MonotonicityNot monotonic
2022-02-18T13:31:27.572880image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
37.87590228
 
0.1%
46.34758219
 
< 0.1%
37.616159912
 
< 0.1%
41.3211
 
< 0.1%
35.931110
 
< 0.1%
46.35100410
 
< 0.1%
37.41987510
 
< 0.1%
47.6589
 
< 0.1%
39.79594468
 
< 0.1%
37.8759858
 
< 0.1%
Other values (47857)50164
99.8%
ValueCountFrequency (%)
18.3341381
< 0.1%
18.3655851
< 0.1%
18.3698731
< 0.1%
18.3753121
< 0.1%
18.4125311
< 0.1%
18.421071
< 0.1%
18.4231351
< 0.1%
19.0627771
< 0.1%
19.5020752
< 0.1%
19.5025181
< 0.1%
ValueCountFrequency (%)
64.8524661
< 0.1%
64.20084131
< 0.1%
63.868924161
< 0.1%
63.4474191
< 0.1%
63.447256021
< 0.1%
62.3239611
< 0.1%
62.32131
< 0.1%
61.6018741
< 0.1%
61.5994151
< 0.1%
61.580287541
< 0.1%

Longitude
Real number (ℝ)

HIGH CORRELATION

Distinct48079
Distinct (%)95.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-98.4699633
Minimum-159.788556
Maximum0
Zeros1
Zeros (%)< 0.1%
Negative50288
Negative (%)> 99.9%
Memory size393.0 KiB
2022-02-18T13:31:27.699800image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Quantile statistics

Minimum-159.788556
5-th percentile-122.416436
Q1-118.333956
median-95.318592
Q3-80.14123
95-th percentile-71.761801
Maximum0
Range159.788556
Interquartile range (IQR)38.192726

Descriptive statistics

Standard deviation19.78163807
Coefficient of variation (CV)-0.2008900725
Kurtosis-1.177389952
Mean-98.4699633
Median Absolute Deviation (MAD)20.016438
Skewness-0.1878016374
Sum-4951955.984
Variance391.3132046
MonotonicityNot monotonic
2022-02-18T13:31:27.819541image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-122.25005528
 
0.1%
-119.27789219
 
< 0.1%
-122.396868712
 
< 0.1%
-72.0711
 
< 0.1%
-84.30997810
 
< 0.1%
-122.20604210
 
< 0.1%
-122.7129
 
< 0.1%
-104.90128858
 
< 0.1%
-122.2500148
 
< 0.1%
-117.3205127
 
< 0.1%
Other values (48069)50167
99.8%
ValueCountFrequency (%)
-159.7885561
< 0.1%
-159.5858781
< 0.1%
-159.5854071
< 0.1%
-159.4851431
< 0.1%
-159.4763931
< 0.1%
-159.469421
< 0.1%
-159.468621
< 0.1%
-159.4567191
< 0.1%
-159.442081
< 0.1%
-159.438971
< 0.1%
ValueCountFrequency (%)
01
< 0.1%
-65.8197481
< 0.1%
-65.9736351
< 0.1%
-66.0663651
< 0.1%
-66.0977461
< 0.1%
-66.1082441
< 0.1%
-66.1122091
< 0.1%
-66.1285431
< 0.1%
-66.983141
< 0.1%
-66.9853711
< 0.1%

Date Last Confirmed
Categorical

HIGH CORRELATION

Distinct39
Distinct (%)0.1%
Missing17
Missing (%)< 0.1%
Memory size393.0 KiB
2022-01-14
32180 
2020-11-03
4428 
2020-06-09
 
1785
2022-01-13
 
1576
2021-10-11
 
1155
Other values (34)
9148 

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)< 0.1%

Sample

1st row2021-07-14
2nd row2021-07-14
3rd row2020-11-09
4th row2021-07-14
5th row2021-07-14

Common Values

ValueCountFrequency (%)
2022-01-1432180
64.0%
2020-11-034428
 
8.8%
2020-06-091785
 
3.5%
2022-01-131576
 
3.1%
2021-10-111155
 
2.3%
2021-04-08945
 
1.9%
2021-02-22868
 
1.7%
2021-10-12799
 
1.6%
2022-01-10669
 
1.3%
2021-01-14604
 
1.2%
Other values (29)5263
 
10.5%

Length

2022-02-18T13:31:27.945767image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2022-01-1432180
64.0%
2020-11-034428
 
8.8%
2020-06-091785
 
3.6%
2022-01-131576
 
3.1%
2021-10-111155
 
2.3%
2021-04-08945
 
1.9%
2021-02-22868
 
1.7%
2021-10-12799
 
1.6%
2022-01-10669
 
1.3%
2021-01-14604
 
1.2%
Other values (29)5263
 
10.5%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

ID
Real number (ℝ≥0)

HIGH CORRELATION
UNIQUE

Distinct50289
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean150156.1131
Minimum1517
Maximum204787
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size393.0 KiB
2022-02-18T13:31:28.056850image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Quantile statistics

Minimum1517
5-th percentile50465.2
Q1115972
median166590
Q3182556
95-th percentile199239.6
Maximum204787
Range203270
Interquartile range (IQR)66584

Descriptive statistics

Standard deviation44819.87924
Coefficient of variation (CV)0.2984885417
Kurtosis-0.09177103594
Mean150156.1131
Median Absolute Deviation (MAD)20640
Skewness-0.9938575048
Sum7551200774
Variance2008821575
MonotonicityStrictly increasing
2022-02-18T13:31:28.181624image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
15171
 
< 0.1%
1776101
 
< 0.1%
1776121
 
< 0.1%
1776131
 
< 0.1%
1776141
 
< 0.1%
1776151
 
< 0.1%
1776161
 
< 0.1%
1776171
 
< 0.1%
1776181
 
< 0.1%
1776191
 
< 0.1%
Other values (50279)50279
> 99.9%
ValueCountFrequency (%)
15171
< 0.1%
15191
< 0.1%
15231
< 0.1%
15251
< 0.1%
15311
< 0.1%
15331
< 0.1%
15521
< 0.1%
15561
< 0.1%
15721
< 0.1%
15731
< 0.1%
ValueCountFrequency (%)
2047871
< 0.1%
2047861
< 0.1%
2047851
< 0.1%
2047841
< 0.1%
2047831
< 0.1%
2047821
< 0.1%
2047811
< 0.1%
2047801
< 0.1%
2047791
< 0.1%
2047781
< 0.1%

Updated At
Categorical

HIGH CARDINALITY

Distinct278
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size393.0 KiB
2022-01-14 00:29:34 UTC
27172 
2021-03-11 23:22:17 UTC
8915 
2022-01-14 00:21:13 UTC
 
1912
2022-01-13 22:55:13 UTC
 
1576
2021-11-04 18:37:47 UTC
 
1153
Other values (273)
9561 

Length

Max length23
Median length23
Mean length23
Min length23

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique232 ?
Unique (%)0.5%

Sample

1st row2021-07-14 18:51:43 UTC
2nd row2021-07-14 18:51:43 UTC
3rd row2021-03-11 23:22:17 UTC
4th row2021-07-14 18:51:43 UTC
5th row2021-07-14 18:51:43 UTC

Common Values

ValueCountFrequency (%)
2022-01-14 00:29:34 UTC27172
54.0%
2021-03-11 23:22:17 UTC8915
 
17.7%
2022-01-14 00:21:13 UTC1912
 
3.8%
2022-01-13 22:55:13 UTC1576
 
3.1%
2021-11-04 18:37:47 UTC1153
 
2.3%
2021-08-04 00:01:52 UTC1074
 
2.1%
2022-01-14 01:20:00 UTC1018
 
2.0%
2022-01-14 01:17:49 UTC868
 
1.7%
2021-04-08 20:32:57 UTC804
 
1.6%
2022-01-14 01:22:29 UTC736
 
1.5%
Other values (268)5061
 
10.1%

Length

2022-02-18T13:31:28.302145image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
utc50289
33.3%
2022-01-1432180
21.3%
00:29:3427172
18.0%
2021-03-118915
 
5.9%
23:22:178915
 
5.9%
00:21:131912
 
1.3%
2022-01-131578
 
1.0%
22:55:131576
 
1.0%
2021-11-041559
 
1.0%
2021-08-041184
 
0.8%
Other values (335)15587
 
10.3%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Owner Type Code
Categorical

HIGH CORRELATION
MISSING

Distinct6
Distinct (%)< 0.1%
Missing33756
Missing (%)67.1%
Memory size393.0 KiB
P
13421 
LG
 
1243
FG
 
1054
T
 
515
SG
 
296

Length

Max length2
Median length1
Mean length1.156837839
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowLG
2nd rowLG
3rd rowP
4th rowLG
5th rowLG

Common Values

ValueCountFrequency (%)
P13421
 
26.7%
LG1243
 
2.5%
FG1054
 
2.1%
T515
 
1.0%
SG296
 
0.6%
J4
 
< 0.1%
(Missing)33756
67.1%

Length

2022-02-18T13:31:28.394628image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-02-18T13:31:28.464550image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
ValueCountFrequency (%)
p13421
81.2%
lg1243
 
7.5%
fg1054
 
6.4%
t515
 
3.1%
sg296
 
1.8%
j4
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Federal Agency ID
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct25
Distinct (%)2.4%
Missing49235
Missing (%)97.9%
Infinite0
Infinite (%)0.0%
Mean13.43263757
Minimum2
Maximum29
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size393.0 KiB
2022-02-18T13:31:28.549360image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile4
Q18
median14
Q316
95-th percentile25
Maximum29
Range27
Interquartile range (IQR)8

Descriptive statistics

Standard deviation5.623238757
Coefficient of variation (CV)0.4186250636
Kurtosis-0.07760730957
Mean13.43263757
Median Absolute Deviation (MAD)3
Skewness0.2115496204
Sum14158
Variance31.62081412
MonotonicityNot monotonic
2022-02-18T13:31:28.643179image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
14221
 
0.4%
16159
 
0.3%
8156
 
0.3%
1294
 
0.2%
1956
 
0.1%
1345
 
0.1%
2642
 
0.1%
1737
 
0.1%
633
 
0.1%
224
 
< 0.1%
Other values (15)187
 
0.4%
(Missing)49235
97.9%
ValueCountFrequency (%)
224
 
< 0.1%
321
 
< 0.1%
421
 
< 0.1%
518
 
< 0.1%
633
 
0.1%
79
 
< 0.1%
8156
0.3%
913
 
< 0.1%
1019
 
< 0.1%
1294
0.2%
ValueCountFrequency (%)
292
 
< 0.1%
2642
0.1%
2520
 
< 0.1%
246
 
< 0.1%
235
 
< 0.1%
2224
< 0.1%
215
 
< 0.1%
2021
 
< 0.1%
1956
0.1%
182
 
< 0.1%

Federal Agency Name
Categorical

HIGH CORRELATION
MISSING

Distinct25
Distinct (%)2.4%
Missing49235
Missing (%)97.9%
Memory size393.0 KiB
Department of Navy
221 
Department of the Interior
159 
U.S. Department of Energy
156 
Department of Justice
94 
Department of Veterans Affairs
56 
Other values (20)
368 

Length

Max length45
Median length25
Mean length24.13851992
Min length16

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.1%

Sample

1st rowSmithsonian Institution
2nd rowDepartment of Veterans Affairs
3rd rowDepartment of the Interior
4th rowNational Aeronautics and Space Administration
5th rowDepartment of the Interior

Common Values

ValueCountFrequency (%)
Department of Navy221
 
0.4%
Department of the Interior159
 
0.3%
U.S. Department of Energy156
 
0.3%
Department of Justice94
 
0.2%
Department of Veterans Affairs56
 
0.1%
Department of Labor45
 
0.1%
United States Marine Corps42
 
0.1%
Department of Transportation37
 
0.1%
Department of Army33
 
0.1%
Corps of Engineers, Civil Works24
 
< 0.1%
Other values (15)187
 
0.4%
(Missing)49235
97.9%

Length

2022-02-18T13:31:28.747954image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
of910
24.2%
department886
23.5%
navy221
 
5.9%
u.s220
 
5.8%
the159
 
4.2%
interior159
 
4.2%
energy156
 
4.1%
justice94
 
2.5%
corps66
 
1.8%
veterans56
 
1.5%
Other values (37)838
22.3%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Open Date
Categorical

HIGH CARDINALITY

Distinct2833
Distinct (%)5.6%
Missing72
Missing (%)0.1%
Memory size393.0 KiB
2021-01-27
10413 
2020-06-12
3434 
2018-11-01
 
905
2021-10-25
 
843
2012-01-31
 
714
Other values (2828)
33908 

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique647 ?
Unique (%)1.3%

Sample

1st row1999-10-15
2nd row2020-02-28
3rd row1995-08-30
4th row1999-10-15
5th row2018-05-01

Common Values

ValueCountFrequency (%)
2021-01-2710413
 
20.7%
2020-06-123434
 
6.8%
2018-11-01905
 
1.8%
2021-10-25843
 
1.7%
2012-01-31714
 
1.4%
2020-11-03677
 
1.3%
2021-01-15654
 
1.3%
2017-09-01534
 
1.1%
2011-03-15470
 
0.9%
2019-08-15335
 
0.7%
Other values (2823)31238
62.1%

Length

2022-02-18T13:31:28.844970image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2021-01-2710413
 
20.7%
2020-06-123434
 
6.8%
2018-11-01905
 
1.8%
2021-10-25843
 
1.7%
2012-01-31714
 
1.4%
2020-11-03677
 
1.3%
2021-01-15654
 
1.3%
2017-09-01534
 
1.1%
2011-03-15470
 
0.9%
2019-08-15335
 
0.7%
Other values (2823)31238
62.2%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Hydrogen Status Link
Unsupported

MISSING
REJECTED
UNSUPPORTED

Missing50289
Missing (%)100.0%
Memory size393.0 KiB

NG Vehicle Class
Unsupported

MISSING
REJECTED
UNSUPPORTED

Missing50289
Missing (%)100.0%
Memory size393.0 KiB

LPG Primary
Unsupported

MISSING
REJECTED
UNSUPPORTED

Missing50289
Missing (%)100.0%
Memory size393.0 KiB

E85 Blender Pump
Unsupported

MISSING
REJECTED
UNSUPPORTED

Missing50289
Missing (%)100.0%
Memory size393.0 KiB

EV Connector Types
Categorical

HIGH CORRELATION

Distinct30
Distinct (%)0.1%
Missing17
Missing (%)< 0.1%
Memory size393.0 KiB
J1772
38784 
TESLA
 
3326
CHADEMO J1772COMBO
 
3004
J1772 TESLA
 
2527
CHADEMO J1772 J1772COMBO
 
641
Other values (25)
 
1990

Length

Max length32
Median length5
Mean length6.53747613
Min length5

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique12 ?
Unique (%)< 0.1%

Sample

1st rowCHADEMO J1772 J1772COMBO
2nd rowJ1772
3rd rowJ1772
4th rowCHADEMO J1772 J1772COMBO
5th rowCHADEMO J1772 J1772COMBO

Common Values

ValueCountFrequency (%)
J177238784
77.1%
TESLA3326
 
6.6%
CHADEMO J1772COMBO3004
 
6.0%
J1772 TESLA2527
 
5.0%
CHADEMO J1772 J1772COMBO641
 
1.3%
CHADEMO J1772409
 
0.8%
J1772COMBO390
 
0.8%
NEMA520242
 
0.5%
NEMA515218
 
0.4%
J1772 NEMA515213
 
0.4%
Other values (20)518
 
1.0%

Length

2022-02-18T13:31:28.945861image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
j177242792
73.8%
tesla5864
 
10.1%
chademo4207
 
7.3%
j1772combo4132
 
7.1%
nema515435
 
0.8%
nema520352
 
0.6%
nema1450176
 
0.3%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Country
Categorical

CONSTANT
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size393.0 KiB
US
50289 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowUS
2nd rowUS
3rd rowUS
4th rowUS
5th rowUS

Common Values

ValueCountFrequency (%)
US50289
100.0%

Length

2022-02-18T13:31:29.046662image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-02-18T13:31:29.106334image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
ValueCountFrequency (%)
us50289
100.0%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Intersection Directions (French)
Unsupported

MISSING
REJECTED
UNSUPPORTED

Missing50289
Missing (%)100.0%
Memory size393.0 KiB

Access Days Time (French)
Unsupported

MISSING
REJECTED
UNSUPPORTED

Missing50289
Missing (%)100.0%
Memory size393.0 KiB

BD Blends (French)
Unsupported

MISSING
REJECTED
UNSUPPORTED

Missing50289
Missing (%)100.0%
Memory size393.0 KiB

Groups With Access Code (French)
Categorical

HIGH CORRELATION

Distinct22
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size393.0 KiB
Public
43929 
Privé
 
2844
Public - Appeler à l'avance
 
1608
Public - Carte de crédit en tout temps
 
1003
Privé - Réservé au gouvernement
 
709
Other values (17)
 
196

Length

Max length70
Median length6
Mean length7.732386804
Min length5

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique6 ?
Unique (%)< 0.1%

Sample

1st rowPrivé
2nd rowPrivé
3rd rowPublic
4th rowPrivé
5th rowPrivé

Common Values

ValueCountFrequency (%)
Public43929
87.4%
Privé2844
 
5.7%
Public - Appeler à l'avance1608
 
3.2%
Public - Carte de crédit en tout temps1003
 
2.0%
Privé - Réservé au gouvernement709
 
1.4%
TEMPORAIREMENT SUSPENDU (Public)56
 
0.1%
PRÉVU - pas encore accessible (Public)52
 
0.1%
Privé - Carte de crédit en tout temps24
 
< 0.1%
PRÉVU - pas encore accessible (Public - Carte de crédit en tout temps)13
 
< 0.1%
Public - Carte-clé en tout temps12
 
< 0.1%
Other values (12)39
 
0.1%

Length

2022-02-18T13:31:29.169415image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
public46686
69.1%
privé3603
 
5.3%
3462
 
5.1%
appeler1619
 
2.4%
à1619
 
2.4%
l'avance1619
 
2.4%
en1054
 
1.6%
tout1054
 
1.6%
temps1054
 
1.6%
de1051
 
1.6%
Other values (21)4703
 
7.0%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Hydrogen Is Retail
Unsupported

MISSING
REJECTED
UNSUPPORTED

Missing50289
Missing (%)100.0%
Memory size393.0 KiB

Access Code
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size393.0 KiB
public
46686 
private
 
3603

Length

Max length7
Median length6
Mean length6.071645887
Min length6

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowprivate
2nd rowprivate
3rd rowpublic
4th rowprivate
5th rowprivate

Common Values

ValueCountFrequency (%)
public46686
92.8%
private3603
 
7.2%

Length

2022-02-18T13:31:29.264453image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-02-18T13:31:29.325327image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
ValueCountFrequency (%)
public46686
92.8%
private3603
 
7.2%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Access Detail Code
Categorical

HIGH CORRELATION
MISSING

Distinct8
Distinct (%)0.2%
Missing46895
Missing (%)93.3%
Memory size393.0 KiB
CALL
1619 
CREDIT_CARD_ALWAYS
1042 
GOVERNMENT
710 
KEY_ALWAYS
 
12
FLEET
 
7
Other values (3)
 
4

Length

Max length23
Median length10
Mean length9.59310548
Min length4

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)0.1%

Sample

1st rowGOVERNMENT
2nd rowGOVERNMENT
3rd rowCALL
4th rowCALL
5th rowGOVERNMENT

Common Values

ValueCountFrequency (%)
CALL1619
 
3.2%
CREDIT_CARD_ALWAYS1042
 
2.1%
GOVERNMENT710
 
1.4%
KEY_ALWAYS12
 
< 0.1%
FLEET7
 
< 0.1%
CREDIT_CARD_AFTER_HOURS2
 
< 0.1%
KEY_AFTER_HOURS1
 
< 0.1%
RESIDENTIAL1
 
< 0.1%
(Missing)46895
93.3%

Length

2022-02-18T13:31:29.399625image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-02-18T13:31:29.478500image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
ValueCountFrequency (%)
call1619
47.7%
credit_card_always1042
30.7%
government710
20.9%
key_always12
 
0.4%
fleet7
 
0.2%
credit_card_after_hours2
 
0.1%
key_after_hours1
 
< 0.1%
residential1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Federal Agency Code
Categorical

HIGH CORRELATION
MISSING

Distinct25
Distinct (%)2.4%
Missing49235
Missing (%)97.9%
Memory size393.0 KiB
DON
221 
DOI
159 
DOE
156 
DOJ
94 
VA
56 
Other values (20)
368 

Length

Max length8
Median length3
Mean length3.126185958
Min length2

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.1%

Sample

1st rowSI
2nd rowVA
3rd rowDOI
4th rowNASA
5th rowDOI

Common Values

ValueCountFrequency (%)
DON221
 
0.4%
DOI159
 
0.3%
DOE156
 
0.3%
DOJ94
 
0.2%
VA56
 
0.1%
DOL45
 
0.1%
USMC42
 
0.1%
DOT37
 
0.1%
DA33
 
0.1%
USACE_CW24
 
< 0.1%
Other values (15)187
 
0.4%
(Missing)49235
97.9%

Length

2022-02-18T13:31:29.593519image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
don221
21.0%
doi159
15.1%
doe156
14.8%
doj94
8.9%
va56
 
5.3%
dol45
 
4.3%
usmc42
 
4.0%
dot37
 
3.5%
da33
 
3.1%
usace_cw24
 
2.3%
Other values (15)187
17.7%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Facility Type
Categorical

HIGH CARDINALITY
HIGH CORRELATION
MISSING

Distinct60
Distinct (%)0.4%
Missing35474
Missing (%)70.5%
Memory size393.0 KiB
HOTEL
2559 
CAR_DEALER
2375 
OFFICE_BLDG
884 
FED_GOV
870 
SHOPPING_CENTER
824 
Other values (55)
7303 

Length

Max length25
Median length10
Mean length9.595342558
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)< 0.1%

Sample

1st rowUTILITY
2nd rowUTILITY
3rd rowPARKING_GARAGE
4th rowUTILITY
5th rowUTILITY

Common Values

ValueCountFrequency (%)
HOTEL2559
 
5.1%
CAR_DEALER2375
 
4.7%
OFFICE_BLDG884
 
1.8%
FED_GOV870
 
1.7%
SHOPPING_CENTER824
 
1.6%
MUNI_GOV686
 
1.4%
UTILITY542
 
1.1%
SHOPPING_MALL474
 
0.9%
COLLEGE_CAMPUS473
 
0.9%
PAY_GARAGE416
 
0.8%
Other values (50)4712
 
9.4%
(Missing)35474
70.5%

Length

2022-02-18T13:31:29.690301image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
hotel2559
17.3%
car_dealer2375
16.0%
office_bldg884
 
6.0%
fed_gov870
 
5.9%
shopping_center824
 
5.6%
muni_gov686
 
4.6%
utility542
 
3.7%
shopping_mall474
 
3.2%
college_campus473
 
3.2%
pay_garage416
 
2.8%
Other values (50)4712
31.8%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

CNG Dispenser Num
Unsupported

MISSING
REJECTED
UNSUPPORTED

Missing50289
Missing (%)100.0%
Memory size393.0 KiB

CNG On-Site Renewable Source
Unsupported

MISSING
REJECTED
UNSUPPORTED

Missing50289
Missing (%)100.0%
Memory size393.0 KiB

CNG Total Compression Capacity
Unsupported

MISSING
REJECTED
UNSUPPORTED

Missing50289
Missing (%)100.0%
Memory size393.0 KiB

CNG Storage Capacity
Unsupported

MISSING
REJECTED
UNSUPPORTED

Missing50289
Missing (%)100.0%
Memory size393.0 KiB

LNG On-Site Renewable Source
Unsupported

MISSING
REJECTED
UNSUPPORTED

Missing50289
Missing (%)100.0%
Memory size393.0 KiB

E85 Other Ethanol Blends
Unsupported

MISSING
REJECTED
UNSUPPORTED

Missing50289
Missing (%)100.0%
Memory size393.0 KiB

EV Pricing
Categorical

HIGH CARDINALITY
MISSING

Distinct564
Distinct (%)3.8%
Missing35416
Missing (%)70.4%
Memory size393.0 KiB
Free
9176 
$0.28 per kWh; $0.26 per minute above 60 kW and $0.13 per minute at or below 60 kW
1155 
FREE
1025 
Level 2: $0.49 per kWh
 
798
Level 2: $0.59 per kWh
 
315
Other values (559)
2404 

Length

Max length126
Median length4
Mean length15.56921939
Min length3

Characters and Unicode

Total characters1
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique355 ?
Unique (%)2.4%

Sample

1st rowFree
2nd rowFree
3rd rowPay to Park
4th rowFree
5th rowFree

Common Values

ValueCountFrequency (%)
Free9176
 
18.2%
$0.28 per kWh; $0.26 per minute above 60 kW and $0.13 per minute at or below 60 kW1155
 
2.3%
FREE1025
 
2.0%
Level 2: $0.49 per kWh798
 
1.6%
Level 2: $0.59 per kWh315
 
0.6%
$2.00/Hr Parking Fee209
 
0.4%
Level 2: $0.03 per 30 seconds204
 
0.4%
$1.00/Hr Parking Fee98
 
0.2%
$1 initiation fee + $0.32 per minute88
 
0.2%
$1.50/Hr Parking Fee67
 
0.1%
Other values (554)1738
 
3.5%
(Missing)35416
70.4%

Length

2022-02-18T13:31:29.818973image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
free10307
21.3%
per5827
 
12.0%
kwh2660
 
5.5%
minute2455
 
5.1%
kw2312
 
4.8%
602311
 
4.8%
21620
 
3.3%
level1577
 
3.3%
fee1496
 
3.1%
or1273
 
2.6%
Other values (478)16648
34.3%

Most occurring characters

ValueCountFrequency (%)
1
100.0%

Most occurring categories

ValueCountFrequency (%)
Control1
100.0%

Most frequent character per category

Control
ValueCountFrequency (%)
1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common1
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII1
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1
100.0%

EV Pricing (French)
Unsupported

MISSING
REJECTED
UNSUPPORTED

Missing50289
Missing (%)100.0%
Memory size393.0 KiB

LPG Nozzle Types
Unsupported

MISSING
REJECTED
UNSUPPORTED

Missing50289
Missing (%)100.0%
Memory size393.0 KiB

Hydrogen Pressures
Unsupported

MISSING
REJECTED
UNSUPPORTED

Missing50289
Missing (%)100.0%
Memory size393.0 KiB

Hydrogen Standards
Unsupported

MISSING
REJECTED
UNSUPPORTED

Missing50289
Missing (%)100.0%
Memory size393.0 KiB

CNG Fill Type Code
Unsupported

MISSING
REJECTED
UNSUPPORTED

Missing50289
Missing (%)100.0%
Memory size393.0 KiB

CNG PSI
Unsupported

MISSING
REJECTED
UNSUPPORTED

Missing50289
Missing (%)100.0%
Memory size393.0 KiB

CNG Vehicle Class
Unsupported

MISSING
REJECTED
UNSUPPORTED

Missing50289
Missing (%)100.0%
Memory size393.0 KiB

LNG Vehicle Class
Unsupported

MISSING
REJECTED
UNSUPPORTED

Missing50289
Missing (%)100.0%
Memory size393.0 KiB

EV On-Site Renewable Source
Categorical

HIGH CORRELATION
MISSING

Distinct6
Distinct (%)1.7%
Missing49937
Missing (%)99.3%
Memory size393.0 KiB
SOLAR
246 
NONE
62 
WIND
30 
HYDRO
 
10
WASTEWATER
 
3

Length

Max length10
Median length5
Mean length4.789772727
Min length4

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.3%

Sample

1st rowSOLAR
2nd rowSOLAR
3rd rowSOLAR
4th rowWIND
5th rowSOLAR

Common Values

ValueCountFrequency (%)
SOLAR246
 
0.5%
NONE62
 
0.1%
WIND30
 
0.1%
HYDRO10
 
< 0.1%
WASTEWATER3
 
< 0.1%
LANDFILL1
 
< 0.1%
(Missing)49937
99.3%

Length

2022-02-18T13:31:30.061534image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-02-18T13:31:30.129354image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
ValueCountFrequency (%)
solar246
69.9%
none62
 
17.6%
wind30
 
8.5%
hydro10
 
2.8%
wastewater3
 
0.9%
landfill1
 
0.3%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Restricted Access
Unsupported

MISSING
REJECTED
UNSUPPORTED

Missing50289
Missing (%)100.0%
Memory size393.0 KiB

Interactions

2022-02-18T13:31:19.543882image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-18T13:31:14.528994image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-18T13:31:15.316087image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-18T13:31:16.241022image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-18T13:31:16.965627image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-18T13:31:17.744962image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-18T13:31:18.556857image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-18T13:31:19.633647image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-18T13:31:14.719500image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-18T13:31:15.422801image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-18T13:31:16.333279image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-18T13:31:17.072342image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-18T13:31:17.857630image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-18T13:31:18.675630image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-18T13:31:19.739361image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-18T13:31:14.823297image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-18T13:31:15.530516image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-18T13:31:16.444023image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-18T13:31:17.176102image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-18T13:31:17.972353image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-18T13:31:18.795337image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-18T13:31:19.837101image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-18T13:31:14.915084image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-18T13:31:15.773869image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-18T13:31:16.545751image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-18T13:31:17.283850image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-18T13:31:18.084057image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-18T13:31:18.920031image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-18T13:31:19.939828image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-18T13:31:15.013822image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-18T13:31:15.894538image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-18T13:31:16.648480image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-18T13:31:17.387619image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-18T13:31:18.204734image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-18T13:31:19.037725image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-18T13:31:20.040555image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-18T13:31:15.111597image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-18T13:31:16.020197image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-18T13:31:16.749209image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-18T13:31:17.519223image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-18T13:31:18.336381image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-18T13:31:19.302313image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-18T13:31:20.155252image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-18T13:31:15.223299image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-18T13:31:16.136919image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-18T13:31:16.864898image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-18T13:31:17.637935image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-18T13:31:18.457089image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-18T13:31:19.427760image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Correlations

2022-02-18T13:31:30.251993image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-02-18T13:31:30.708004image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-02-18T13:31:31.171288image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-02-18T13:31:31.620127image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-02-18T13:31:20.722370image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2022-02-18T13:31:22.784224image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.
2022-02-18T13:31:23.438119image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
The dendrogram allows you to more fully correlate variable completion, revealing trends deeper than the pairwise ones visible in the correlation heatmap.

Sample

First rows

Fuel Type CodeStation NameStreet AddressIntersection DirectionsCityStateZIPPlus4Station PhoneStatus CodeExpected DateGroups With Access CodeAccess Days TimeCards AcceptedBD BlendsNG Fill Type CodeNG PSIEV Level1 EVSE NumEV Level2 EVSE NumEV DC Fast CountEV Other InfoEV NetworkEV Network WebGeocode StatusLatitudeLongitudeDate Last ConfirmedIDUpdated AtOwner Type CodeFederal Agency IDFederal Agency NameOpen DateHydrogen Status LinkNG Vehicle ClassLPG PrimaryE85 Blender PumpEV Connector TypesCountryIntersection Directions (French)Access Days Time (French)BD Blends (French)Groups With Access Code (French)Hydrogen Is RetailAccess CodeAccess Detail CodeFederal Agency CodeFacility TypeCNG Dispenser NumCNG On-Site Renewable SourceCNG Total Compression CapacityCNG Storage CapacityLNG On-Site Renewable SourceE85 Other Ethanol BlendsEV PricingEV Pricing (French)LPG Nozzle TypesHydrogen PressuresHydrogen StandardsCNG Fill Type CodeCNG PSICNG Vehicle ClassLNG Vehicle ClassEV On-Site Renewable SourceRestricted Access
0ELECLADWP - Truesdale Center11797 Truesdale StNaNSun ValleyCA91352NaNNaNENaNPrivateFleet use onlyNaNNaNNaNNaNNaN39.03.0NaNNon-NetworkedNaNGPS34.248319-118.3879712021-07-1415172021-07-14 18:51:43 UTCLGNaNNaN1999-10-15NaNNaNNaNNaNCHADEMO J1772 J1772COMBOUSNaNNaNNaNPrivéNaNprivateNaNNaNUTILITYNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
1ELECLADWP - West LA District Office1394 S Sepulveda BlvdNaNLos AngelesCA90024NaNNaNENaNPrivateNaNNaNNaNNaNNaNNaN4.0NaNNaNNon-NetworkedNaN200-834.052542-118.4485042021-07-1415192021-07-14 18:51:43 UTCLGNaNNaN2020-02-28NaNNaNNaNNaNJ1772USNaNNaNNaNPrivéNaNprivateNaNNaNUTILITYNaNNaNNaNNaNNaNNaNFreeNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
2ELECLos Angeles Convention Center1201 S Figueroa StWest hall and South hallLos AngelesCA90015NaN213-741-1151ENaNPublic24 hours daily; pay lotNaNNaNNaNNaNNaN12.0NaNNaNNon-NetworkedNaNGPS34.040539-118.2713872020-11-0915232021-03-11 23:22:17 UTCPNaNNaN1995-08-30NaNNaNNaNNaNJ1772USNaNNaNNaNPublicNaNpublicNaNNaNPARKING_GARAGENaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
3ELECLADWP - John Ferraro Building111 N Hope StAcross HopeLos AngelesCA90012NaNNaNENaNPrivateFor fleet and employee use onlyNaNNaNNaNNaNNaN311.02.0NaNNon-NetworkedNaNGPS34.059133-118.2485892021-07-1415252021-07-14 18:51:43 UTCLGNaNNaN1999-10-15NaNNaNNaNNaNCHADEMO J1772 J1772COMBOUSNaNNaNNaNPrivéNaNprivateNaNNaNUTILITYNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
4ELECLADWP - Haynes Power Plant6801 E 2nd StNaNLong BeachCA90803NaNNaNENaNPrivateFleet use onlyNaNNaNNaNNaNNaN19.01.0NaNNon-NetworkedNaNGPS33.759802-118.0966652021-07-1415312021-07-14 18:51:43 UTCLGNaNNaN2018-05-01NaNNaNNaNNaNCHADEMO J1772 J1772COMBOUSNaNNaNNaNPrivéNaNprivateNaNNaNUTILITYNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
5ELECState Capitol Parking Garage1303 10th StAt 12th and N StSacramentoCA95814NaNNaNENaNPrivate - Government onlyPermitted state legislators onlyComdataNaNNaNNaNNaN9.0NaN1 LP Inductive 1 SP Inductive 1 Avcon Conductive 1 Other ConductiveNon-NetworkedNaN200-838.576769-121.4950222021-12-0915332021-12-09 15:02:43 UTCSGNaNNaN1996-10-15NaNNaNNaNNaNJ1772USNaNNaNNaNPrivé - Réservé au gouvernementNaNprivateGOVERNMENTNaNPARKING_GARAGENaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
6ELECLADWP - Harbor Generating Station161 N Island AveAt B StWilmingtonCA90744NaNNaNENaNPrivateFleet use onlyNaNNaNNaNNaNNaN10.0NaNNaNNon-NetworkedNaN200-833.770508-118.2656282021-07-1415522021-07-14 18:51:43 UTCLGNaNNaN1999-10-15NaNNaNNaNNaNJ1772USNaNNaNNaNPrivéNaNprivateNaNNaNUTILITYNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
7ELECLADWP - Sylmar West13201 Sepulveda BlvdNaNSylmarCA91342NaNNaNENaNPrivate - Government onlyFleet use onlyNaNNaNNaNNaNNaN2.0NaNNaNNon-NetworkedNaN200-834.303090-118.4805052021-07-1415562021-07-14 18:51:43 UTCLGNaNNaN2016-01-01NaNNaNNaNNaNJ1772USNaNNaNNaNPrivé - Réservé au gouvernementNaNprivateGOVERNMENTNaNUTILITYNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
8ELECLADWP - EV Service Center1630 N Main StNaNLos AngelesCA90012NaNNaNENaNPrivateFleet and employee use onlyNaNNaNNaNNaNNaN46.01.0NaNNon-NetworkedNaN200-834.066801-118.2276052021-07-1415722021-07-14 18:51:43 UTCLGNaNNaN1999-10-15NaNNaNNaNNaNCHADEMO J1772USNaNNaNNaNPrivéNaNprivateNaNNaNUTILITYNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
9ELECLADWP - Fairfax Center2311 S Fairfax AveNaNLos AngelesCA90016NaNNaNENaNPrivateFleet use onlyNaNNaNNaNNaNNaN13.0NaNNaNNon-NetworkedNaN200-834.036777-118.3688412021-07-1415732021-07-14 18:51:43 UTCLGNaNNaN2019-04-01NaNNaNNaNNaNJ1772USNaNNaNNaNPrivéNaNprivateNaNNaNUTILITYNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN

Last rows

Fuel Type CodeStation NameStreet AddressIntersection DirectionsCityStateZIPPlus4Station PhoneStatus CodeExpected DateGroups With Access CodeAccess Days TimeCards AcceptedBD BlendsNG Fill Type CodeNG PSIEV Level1 EVSE NumEV Level2 EVSE NumEV DC Fast CountEV Other InfoEV NetworkEV Network WebGeocode StatusLatitudeLongitudeDate Last ConfirmedIDUpdated AtOwner Type CodeFederal Agency IDFederal Agency NameOpen DateHydrogen Status LinkNG Vehicle ClassLPG PrimaryE85 Blender PumpEV Connector TypesCountryIntersection Directions (French)Access Days Time (French)BD Blends (French)Groups With Access Code (French)Hydrogen Is RetailAccess CodeAccess Detail CodeFederal Agency CodeFacility TypeCNG Dispenser NumCNG On-Site Renewable SourceCNG Total Compression CapacityCNG Storage CapacityLNG On-Site Renewable SourceE85 Other Ethanol BlendsEV PricingEV Pricing (French)LPG Nozzle TypesHydrogen PressuresHydrogen StandardsCNG Fill Type CodeCNG PSICNG Vehicle ClassLNG Vehicle ClassEV On-Site Renewable SourceRestricted Access
50279ELECAIR PRODUCTS STATION11940 Air Products BlvdNaNWescosvillePA18106NaN888-758-4389ENaNPublic24 hours dailyNaNNaNNaNNaNNaN2.0NaNNaNChargePoint Networkhttp://www.chargepoint.com/GPS40.559613-75.5789752022-01-142047782022-01-14 00:29:34 UTCNaNNaNNaN2022-01-14NaNNaNNaNNaNJ1772USNaNNaNNaNPublicNaNpublicNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
50280ELECMTA MARC GERMANTOWN19421 Walter Johnson RdNaNGermantownMD20874NaN888-758-4389ENaNPublic24 hours dailyNaNNaNNaNNaNNaN2.0NaNNaNChargePoint Networkhttp://www.chargepoint.com/GPS39.174382-77.2703152022-01-142047792022-01-14 00:29:34 UTCNaNNaNNaN2022-01-14NaNNaNNaNNaNJ1772USNaNNaNNaNPublicNaNpublicNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
50281ELEC8 N PLAINS RURAL ACTION8 N Plains RdNaNThe PlainsOH45780NaN888-758-4389ENaNPublicMon 7:00am - 7:00pm; Tue 7:00am - 7:00pm; Wed 7:00am - 7:00pm; Thu 7:00am - 7:00pm; Fri 7:00am - 7:00pm; Sat 7:00am - 7:00pm; Sun 7:00am - 7:00pmNaNNaNNaNNaNNaN2.0NaNNaNChargePoint Networkhttp://www.chargepoint.com/GPS39.369901-82.1328422022-01-142047802022-01-14 00:29:34 UTCNaNNaNNaN2022-01-14NaNNaNNaNNaNJ1772USNaNNaNNaNPublicNaNpublicNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
50282ELECMIDSOUTH CHARGE EMPLOYEE PARK 39339 N Hwy 6 LoopNaNNavasotaTX77868NaN888-758-4389ENaNPublic24 hours dailyNaNNaNNaNNaNNaN1.0NaNNaNChargePoint Networkhttp://www.chargepoint.com/GPS30.389411-96.0680442022-01-142047812022-01-14 00:29:34 UTCNaNNaNNaN2022-01-14NaNNaNNaNNaNJ1772USNaNNaNNaNPublicNaNpublicNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
50283ELECMIDSOUTH CHARGE EMPLOYEE PARK 49339 N Hwy 6 LoopNaNNavasotaTX77868NaN888-758-4389ENaNPublic24 hours dailyNaNNaNNaNNaNNaN1.0NaNNaNChargePoint Networkhttp://www.chargepoint.com/GPS30.389559-96.0681192022-01-142047822022-01-14 00:29:34 UTCNaNNaNNaN2022-01-14NaNNaNNaNNaNJ1772USNaNNaNNaNPublicNaNpublicNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
50284ELECVIP LOT STATION11501 Kirkwood Meadows DrNaNMarkleevilleCA96120NaN888-758-4389ENaNPublic24 hours dailyNaNNaNNaNNaNNaN2.0NaNNaNChargePoint Networkhttp://www.chargepoint.com/GPS38.684660-120.0651692022-01-142047832022-01-14 00:29:34 UTCNaNNaNNaN2022-01-14NaNNaNNaNNaNJ1772USNaNNaNNaNPublicNaNpublicNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
50285ELECPrunedale Shopping Center8065 San Miguel Canyon RdNaNSalinasCA93907NaN877-455-3833ENaNPublic24 hours dailyNaNNaNNaNNaNNaNNaN6.0NaNeVgo Networkhttps://www.evgo.com/GPS36.801716-121.6641532022-01-142047842022-01-14 01:17:49 UTCNaNNaNNaN2022-01-14NaNNaNNaNNaNCHADEMO J1772COMBOUSNaNNaNNaNPublicNaNpublicNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
50286ELECBeaverton Electric Avenue11665 SW Beaverton Hillsdale HwyNaNBeavertonOR97005NaN855-900-7584ENaNPublic24 hours dailyNaNNaNNaNNaNNaNNaN2.0NaNGreenlotshttp://greenlots.com/GPS45.489030-122.7981512022-01-142047852022-01-14 01:20:00 UTCNaNNaNNaN2022-01-14NaNNaNNaNNaNCHADEMO J1772COMBOUSNaNNaNNaNPublicNaNpublicNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
50287ELECShell - Inman2040 Highway 292NaNInmanSC29349NaN855-900-7584ENaNPublic24 hours dailyNaNNaNNaNNaNNaNNaN1.0NaNGreenlotshttp://greenlots.com/GPS35.082476-82.0584332022-01-142047862022-01-14 01:20:00 UTCNaNNaNNaN2022-01-14NaNNaNNaNNaNCHADEMO J1772COMBOUSNaNNaNNaNPublicNaNpublicNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
50288ELECWestfield Trumbull (Trumbull, CT)5065 Main StreetNaNTrumbullCT06611NaN833-632-2778ENaNPublic24 hours dailyNaNNaNNaNNaNNaNNaN4.0NaNElectrify Americahttps://www.electrifyamerica.com/GPS41.228951-73.2239442022-01-142047872022-01-14 01:22:29 UTCNaNNaNNaN2022-01-14NaNNaNNaNNaNCHADEMO J1772COMBOUSNaNNaNNaNPublicNaNpublicNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN